Skip to main content

Unveiling Hidden Neural Codes: SIMPL – A Scalable and Fast Approach for Optimizing Latent Variables and Tuning Curves in Neural Population Data

Dr. Rishabh Pathak
This research paper presents SIMPL (Scalable Iterative Maximization of Population-coded Latents), a novel, computationally efficient algorithm designed to refine the estimation of latent variables and tuning curves from neural population activity. Latent variables in neural data represent essential low-dimensional quantities encoding behavioral or cognitive states, which neuroscientists seek to identify to understand brain computations better. Background and Motivation Traditional approaches commonly assume the observed behavioral variable as the latent neural code. However, this assumption can lead to inaccuracies because neural activity sometimes encodes internal cognitive states differing subtly from observable behavior (e.g., anticipation, mental simulation). Existing latent variable models face challenges such as high computational cost, poor scalability to large datasets, limited expressiveness of tuning models, or difficulties interpreting complex neural network-based functio...
Post a Comment

Indirect Waves (I-Waves)

Image
Dr. Rishabh Pathak

Indirect Waves (I-Waves) are a concept in the field of transcranial magnetic stimulation (TMS) that play a crucial role in understanding the mechanisms of cortical activation and neural responses to magnetic stimulation. Here is an overview of Indirect Waves (I-Waves) and their significance in TMS research:


1.      Definition:

o Indirect Waves (I-Waves) refer to neural responses evoked by transcranial magnetic stimulation that are believed to result from the activation of interneurons in the cortex rather than direct activation of pyramidal neurons.

2.     Mechanism:

o  When a magnetic pulse is applied to the motor cortex using TMS, it can lead to the generation of different types of waves in the corticospinal pathway.

o Indirect Waves (I-Waves) are thought to represent the indirect activation of cortical interneurons, particularly in layer II and III, which then influence the excitability of pyramidal neurons in layer V.

3.     Generation:

o    I-Waves are generated through a complex interaction of the magnetic field with neural elements in the cortex, leading to the recruitment of interneurons and the propagation of neural activity along cortical circuits.

o  These waves are believed to contribute to the modulation of cortical excitability and the generation of motor responses following TMS.

4.    Role in Cortical Activation:

o  I-Waves are essential for understanding the mechanisms of cortical activation and the spread of neural activity following TMS.

o    They are part of the cascade of neural events that occur in response to magnetic stimulation and contribute to the overall effect on motor output and cortical plasticity.

5.     Relationship to Direct Waves (D-Waves):

o  In contrast to Indirect Waves (I-Waves), Direct Waves (D-Waves) are thought to result from the direct activation of pyramidal neurons, particularly in layer V, by the magnetic field generated during TMS.

o  The interplay between I-Waves and D-Waves provides insights into the complex neural dynamics underlying TMS-induced cortical responses.

6.    Research Significance:

o  Studying Indirect Waves (I-Waves) is important for elucidating the neural mechanisms of TMS effects on cortical circuits, motor function, and plasticity.

o By investigating the characteristics and modulation of I-Waves, researchers can gain a deeper understanding of how TMS influences neural activity and connectivity in the brain.

In summary, Indirect Waves (I-Waves) represent a key aspect of neural responses to transcranial magnetic stimulation, reflecting the activation of interneurons and the propagation of neural activity in cortical circuits. Understanding the role of I-Waves is essential for unraveling the complex mechanisms of TMS-induced cortical activation and its implications for brain function and plasticity.

 

 

Categories:

Comments

Post a Comment

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Dr. Rishabh Pathak
Core Concept The relationship between model complexity and dataset size is fundamental in supervised learning, affecting how well a model can learn and generalize. Model complexity refers to the capacity or flexibility of the model to fit a wide variety of functions. Dataset size refers to the number and diversity of training samples available for learning. Key Points 1. Larger Datasets Allow for More Complex Models When your dataset contains more varied data points , you can afford to use more complex models without overfitting. More data points mean more information and variety, enabling the model to learn detailed patterns without fitting noise. Quote from the book: "Relation of Model Complexity to Dataset Size. It’s important to note that model complexity is intimately tied to the variation of inputs contained in your training dataset: the larger variety of data points your dataset contains, the more complex a model you can use without overfitting....
Post a Comment
Read more

Linear Models

Dr. Rishabh Pathak
1. What are Linear Models? Linear models are a class of models that make predictions using a linear function of the input features. The prediction is computed as a weighted sum of the input features plus a bias term. They have been extensively studied over more than a century and remain widely used due to their simplicity, interpretability, and effectiveness in many scenarios. 2. Mathematical Formulation For regression , the general form of a linear model's prediction is: y^ ​ = w0 ​ x0 ​ + w1 ​ x1 ​ + … + wp ​ xp ​ + b where; y^ ​ is the predicted output, xi ​ is the i-th input feature, wi ​ is the learned weight coefficient for feature xi ​ , b is the intercept (bias term), p is the number of features. In vector form: y^ ​ = wTx + b where w = ( w0 ​ , w1 ​ , ... , wp ​ ) and x = ( x0 ​ , x1 ​ , ... , xp ​ ) . 3. Interpretation and Intuition The prediction is a linear combination of features — each feature contributes prop...
Post a Comment
Read more

EEG Amplification

Dr. Rishabh Pathak
EEG amplification, also known as gain or sensitivity, plays a crucial role in EEG recordings by determining the magnitude of electrical signals detected by the electrodes placed on the scalp. Here is a detailed explanation of EEG amplification: 1. Amplification Settings : EEG machines allow for adjustment of the amplification settings, typically measured in microvolts per millimeter (μV/mm). Common sensitivity settings range from 5 to 10 μV/mm, but a wider range of settings may be used depending on the specific requirements of the EEG recording. 2. High-Amplitude Activity : When high-amplitude signals are present in the EEG, such as during epileptiform discharges or artifacts, it may be necessary to compress the vertical display to visualize the full range of each channel within the available space. This compression helps prevent saturation of the signal and ensures that all amplitude levels are visible. 3. Vertical Compression : Increasing the sensitivity value (e.g., from 10 μV/mm to...
Post a Comment
Read more

Mesencephalic Locomotor Region (MLR)

Dr. Rishabh Pathak
The Mesencephalic Locomotor Region (MLR) is a region in the midbrain that plays a crucial role in the control of locomotion and rhythmic movements. Here is an overview of the MLR and its significance in neuroscience research and motor control: 1.       Location : o The MLR is located in the mesencephalon, specifically in the midbrain tegmentum, near the aqueduct of Sylvius. o   It encompasses a group of neurons that are involved in coordinating and modulating locomotor activity. 2.      Function : o   Control of Locomotion : The MLR is considered a key center for initiating and regulating locomotor movements, including walking, running, and other rhythmic activities. o Rhythmic Movements : Neurons in the MLR are involved in generating and coordinating rhythmic patterns of muscle activity essential for locomotion. o Integration of Sensory Information : The MLR receives inputs from various sensory modalities and higher brain regions t...
Post a Comment
Read more

Low-Voltage EEG and Electrocerebral Inactivity

Dr. Rishabh Pathak
Low-voltage EEG and electrocerebral inactivity are important concepts in the assessment of brain function, particularly in the context of diagnosing conditions such as brain death or severe neurological impairment. Here’s an overview of these concepts: 1. Low-Voltage EEG A low-voltage EEG is characterized by a reduced amplitude of electrical activity recorded from the brain. This can be indicative of various neurological conditions, including metabolic disturbances, diffuse brain injury, or encephalopathy. In a low-voltage EEG, the highest amplitude activity is often minimal, typically measuring 2 µV or less, and may primarily consist of artifacts rather than genuine brain activity 37. 2. Electrocerebral Inactivity Electrocerebral inactivity refers to a state where there is a complete absence of detectable electrical activity in the brain. This is a critical finding in the context of determining brain d...
Post a Comment
Read more